Atmospheric Pollution Research最新文献_第10页

A proposed framework based on traffic volume and the design of a bottom-up methodology to estimate the CO emission of on-road vehicles 提出一个以交通量为基础的框架，并设计一个自下而上的方法来估计道路上车辆的二氧化碳排放量

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-14 DOI: 10.1016/j.apr.2025.102699

Shan-Yi Shen , Ru-Yun Chang , Yun-Wei Liao , Wei-Chun Chou , Yu-Hao Lin , Ping-Yu Liu

Providing a high-resolution spatiotemporal concentration of traffic pollutants can support more effective traffic pollution control. The concentration of on-road carbon monoxide (CO) originating from vehicle engine combustion usually has a high positive correlation with traffic volume. Hence, the innovative development of this On-road Vehicle Emission Estimation Model (OVEEM), built with a bottom-up framework, aimed to deliver the high-resolution spatiotemporal data on CO distribution. Based on the technology of web crawler and deep learning, OVEEM not only collect public vehicle detectors and public traffic Surveillance Videos (SVs) but also estimate both the traffic volume and the average velocities of vehicles. The hourly CO emissions can be estimated by multiplying the hourly traffic volume by the CO Emission Factor (EF) corresponding to the average vehicle speed. The emissions from total vehicles were input into the AERMOD dispersion model to estimate the spatiotemporal distributions of CO concentrations. Finally, GIS was employed to visualize the high-resolution spatiotemporal distributions of CO concentrations.

The result indicates that both the observed and the estimated concentrations of CO followed similar trends over the period of 24 h, with a reasonable mean absolute percent error between them. These findings validated the proposed OVEEM through a comparison between the observed and the estimated CO concentrations. Also, scooters and sedans were found to be the main types of vehicles contributing to elevated CO concentrations. In the future, to estimate the distribution of other pollutants, more appropriate SVs should be obtained.

提供高分辨率的交通污染物时空浓度可以支持更有效的交通污染控制。汽车发动机燃烧产生的道路一氧化碳浓度通常与交通量呈高度正相关。基于此，创新开发了基于自底向上框架的道路车辆排放估算模型（OVEEM），旨在提供高分辨率的CO分布时空数据。基于网络爬虫和深度学习技术，OVEEM不仅收集公共车辆检测器和公共交通监控视频（SVs），而且还可以估计车辆的交通量和平均速度。每小时的二氧化碳排放量可由每小时交通量乘以与平均车速相对应的二氧化碳排放系数（EF）来估算。将车辆排放总量输入到AERMOD弥散模型中，估算CO浓度的时空分布。最后，利用GIS对高分辨率的CO浓度时空分布进行可视化处理。结果表明，在24 h的时间内，CO的观测值和估定值的变化趋势相似，两者之间的平均绝对误差在合理范围内。通过比较观测到的CO浓度和估计的CO浓度，这些发现证实了提出的OVEEM。此外，摩托车和轿车被发现是导致二氧化碳浓度升高的主要车辆类型。今后，为了估算其他污染物的分布，需要获得更合适的sv。

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引用次数: 0

Air pollution levels in American Indian communities in the Great Plains and Southwest: The Strong Heart Study 美国大平原和西南地区印第安人社区的空气污染水平：强心脏研究

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-12-11 DOI: 10.1016/j.apr.2025.102860

Jada L. Brooks , Anne Weaver , Baiming Zou , Maggie Li , Jessica A. Reese , Cavin K. Ward-Caviness , Joseph Yracheta , Susan B. Racette , Kimberly Malloy , Ying Zhang , Nora Franceschini , Giselle M. Corbie , Marianthi-Anna Kioumourtzoglou , Gail Currin Fallon , Ana Navas-Acien

Differences in environmental exposures—particularly to ambient air pollution—have been consistently documented across communities in the United States (U.S.). However, relatively few studies on air pollution have focused on American Indians. In this paper, we combine demographic data from Phase IV (2001–2003) and Phase V (2006–2009) of the Strong Heart Study—the largest ongoing longitudinal epidemiologic cohort study of cardiovascular disease in American Indians—and 1 km² modeled PM_2.5 data in the Northern Plains, Southern Plains, and Southwestern United States. We analyzed data at the U.S. ZIP code level to estimate 30-day and annual residential exposures of participants to PM_2.5 and assess for regional, temporal, and seasonal variations in PM_2.5 exposure. We found significantly higher mean 30-day and annual PM_2.5 concentrations among participants in the Southwest and Southern Plains (>7.4 μg/m³ and >7.6 μg/m³, respectively) than in the Northern Plains (<6.6 μg/m³ and <6.2 μg/m³, respectively). We observed heterogeneity in participants' mean 30-day and annual PM_2.5 exposure within and across regions. Furthermore, seasonal differences in ambient PM_2.5 concentrations among participants varied by region; in the Northern Plains, we generally observed higher mean 30-day PM_2.5 exposure levels in the summertime and lower levels in the wintertime, whereas levels remained relatively constant in the Southern Plains and Southwest. These findings offer essential baseline data to advance accurate and equitable exposure assessment across tribal communities in the Northern Plains, Southern Plains, and Southwest regions.

环境暴露的差异，特别是对环境空气污染的差异，在美国的各个社区都有一致的记录。然而，针对美国印第安人的空气污染研究相对较少。在本文中，我们结合了美国印第安人心血管疾病最大的纵向流行病学队列研究——强心脏研究第四阶段（2001-2003年）和第五阶段（2006-2009年）的人口统计数据，以及美国北部平原、南部平原和西南部1平方公里PM2.5模型数据。我们分析了美国邮政编码水平的数据，以估计参与者对PM2.5的30天和年度居住暴露，并评估PM2.5暴露的区域、时间和季节变化。我们发现西南和南部平原参与者的平均30天和年PM2.5浓度（分别为>；7.4 μg/m3和>；7.6 μg/m3）显著高于北部平原参与者（分别为<；6.6 μg/m3和<；6.2 μg/m3）。我们观察到参与者在区域内和跨区域的平均30天和年PM2.5暴露量的异质性。此外，参与者环境PM2.5浓度的季节性差异因地区而异；在北部平原，我们通常观察到夏季较高的平均30天PM2.5暴露水平，冬季较低，而南部平原和西南部的水平保持相对稳定。这些发现为促进北部平原、南部平原和西南地区部落社区的准确和公平的暴露评估提供了基本的基线数据。

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引用次数: 0

Advancing data processing for microplastics characterization: Laser direct infrared (LDIR) analysis of atmospheric deposition in Cienfuegos, Cuba 微塑料表征的先进数据处理：激光直接红外（LDIR）分析古巴西恩富戈斯大气沉积

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-23 DOI: 10.1016/j.apr.2025.102715

Yasser Morera-Gómez , Marco Antonio García-Varens , Bárbaro Miguel Pescoso-Torres , Yusmila Helguera-Pedraza , Arianna García-Chamero , Abel Alonso-Morejón , Nathalie Bernard , Carlos M. Alonso-Hernández

This study developed and applied an automated workflow for post-processing laser direct infrared (LDIR) data, enabling efficient microplastics characterization, including particle count, size, shape, chemical composition, mass, and error estimation. The method was used to evaluate atmospheric microplastics in a rural and an urban coastal environment in Cienfuegos, Cuba, providing valuable insights into microplastic loads and addressing gaps in quantification methods. A total of 11 different synthetic polymers, consistently representing less than 6 % of the total collected particles across both sites, were identified. Polyamide, polypropylene, polyurethane, and polyethylene, accounting for over 55 % of the particles in each sample, were the predominant polymers at both sites. Size and shape analysis revealed that most particles were smaller than 100 μm (>75 %), with low variability between the studied sites. Atmospheric deposition rates exhibited significant monthly variability (23–260 microplastics m⁻² day⁻¹), and mass deposition rates suggested that Cienfuegos may experience an annual microplastic discharge of 1.4–4.4 kg km⁻² consistent with findings from other regions of the world. While the study emphasizes the need for further research to refine methodologies, it fills crucial gaps by examining microplastics in typically understudied areas and achieving a more comprehensive and harmonized assessment of microplastics in environmental studies.

本研究开发并应用了激光直接红外（LDIR）数据后处理的自动化工作流程，实现了高效的微塑料表征，包括颗粒计数、大小、形状、化学成分、质量和误差估计。该方法被用于评估古巴西恩富戈斯农村和城市沿海环境中的大气微塑料，为微塑料负荷提供了有价值的见解，并解决了量化方法的空白。总共鉴定了11种不同的合成聚合物，在两个地点收集的颗粒总数中始终占不到6%。聚酰胺、聚丙烯、聚氨酯和聚乙烯占每个样品中55%以上的颗粒，是两个地点的主要聚合物。尺寸和形状分析显示，大多数颗粒小于100 μm (> 75%)，研究位点之间的差异很小。大气沉积速率表现出显著的月变化（23-260微塑料m−2天−1），质量沉积速率表明西恩富戈斯可能经历1.4-4.4 kg km−2的年微塑料排放，这与世界其他地区的发现一致。虽然该研究强调需要进一步研究以完善方法，但它通过在通常研究不足的领域检查微塑料，并在环境研究中对微塑料进行更全面和协调的评估，填补了关键的空白。

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引用次数: 0

Estimating daily surface O3 concentrations in China from 2005 to 2023 based on the STMO3Net model 基于STMO3Net模式的2005 - 2023年中国地表O3日浓度估算

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-21 DOI: 10.1016/j.apr.2025.102704

Qiaolin Zeng , Yaoyu Qi , Meng Fan , Liangfu Chen , Jinhua Tao , Hao Zhu , Sizhu Liu , Yuanyuan Zhu

In recent years, ground-level ozone (O₃) has replaced particulate matter (PM_2.5) as a major air pollution concern. O₃ concentrations rise sharply during periods of high temperature, posing increasing risks to public health. Previous studies have relied heavily on machine learning to estimate ground-level O₃ concentrations, but these approaches inadequately capture spatiotemporal characteristics. Moreover, the lack of ground-level O₃ monitoring data before 2013 in China has hindered long-term trend studies. To address these issues, this study developed a hybrid spatiotemporal framework that used a point-plane approach to estimate the ground-level O₃ concentrations, named STMO3Net. The model integrated a Transformer-based multi-head self-attention to capture long-range temporal dependencies, and a temporal convolutional network was introduced to improve sensitivity of short-term variations. For spatial modelling, STMO3Net incorporated residual blocks and coordinate-based spatial attention to adaptively adjust the importance of each grid cell based on its spatial position. Additionally, a channel attention module was combined with multi-scale asymmetric convolutions using different kernel sizes to capture spatial features at various scales and enhance feature fusion. The R² (RMSE) of 0.92 (12.27

μ g / m^{3}

) was obtained by the sample-based cross-validation. Using Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument (TROPOMI) satellite data, the model estimated daily ground-level O₃ concentrations over China from 2005 to 2023.

近年来，地面臭氧（O3）已取代颗粒物（PM2.5）成为主要的空气污染问题。在高温时期，臭氧浓度急剧上升，对公众健康构成越来越大的风险。以前的研究严重依赖于机器学习来估计地面臭氧浓度，但这些方法不能充分捕捉时空特征。此外，中国缺乏2013年之前的地面O3监测数据，阻碍了长期趋势研究。为了解决这些问题，本研究开发了一个混合时空框架，使用点平面方法来估计地面臭氧浓度，名为STMO3Net。该模型集成了基于transformer的多头自关注来捕获长期时间依赖性，并引入了时间卷积网络来提高短期变化的灵敏度。对于空间建模，STMO3Net结合残差块和基于坐标的空间注意，根据每个网格单元的空间位置自适应调整其重要性。此外，将通道关注模块与不同核大小的多尺度非对称卷积相结合，捕获不同尺度的空间特征，增强特征融合。经样本交叉验证，R2 （RMSE）为0.92 （12.27 μg/m3）。该模式利用臭氧监测仪和对流层监测仪（TROPOMI）卫星数据，估算了2005 - 2023年中国地面臭氧的日浓度。

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引用次数: 0

Modeling the mass transfer coefficients (MTCs) of PCBs, PAHs, OCPs, and PCDD/Fs using a water surface sampler (WSS) 利用水面采样器（WSS）模拟多氯联苯、多环芳烃、OCPs和PCDD/Fs的传质系数（MTCs）

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-26 DOI: 10.1016/j.apr.2025.102718

Abdul Alim Noori, Berke Gülegen, Yücel Tasdemir

In this study, the mass transfer coefficients (MTCs) characterizing the air-water exchange of PCDD/Fs were investigated. Evaporation and dissolved oxygen (DO) experiments were carried out using a water surface sampler (WSS), and the MTCs of semi-volatile organic compounds (SVOCs) were then modeled based on the data obtained from these experiments. As a result of the calculations made using the evaporation flux observed in the WSS, the average air-side individual MTC [k_g _(H2O)] was determined as 1.33 ± 0.58 cm/s k_{g (H2O)} values were correlated against u_10, and a model for the WSS was derived. The water-side individual MTC [(k_w _(O2)] for oxygen was calculated using two methods, flux and absorption. According to the results of the calculations, k_w _(O2) values were determined as 9.35 × 10⁻⁴ ±7.65 × 10⁻¹ cm/s and 1.30 × 10⁻³ ±6.60 × 10⁻¹ cm/s by flux and absorption methods, respectively. The obtained k_{w (O2)} values were also correlated against u₁₀, and k_{w(O2)_flux} and k_{w(O2)_abs} models were constructed for the WSS. Consequently, by applying the findings to the two-film theory, the overall MTC [K_g _(SVOCs)] in the gas phase was calculated as 0.03 ± 0.03 cm/s for PCBs, 0.19 ± 0.18 cm/s for PAHs, 0.11 ± 0.08 cm/s for OCPs, and 0.07 ± 0.01 cm/s for PCDD/Fs. MTC values showed seasonal variation, with higher values observed during cold periods. There was also an oscillation in the MTCs among the species of SVOCs. When the chlorine number and molecular weight increased, the MTCs also increased. The results found by modeling are in line with the measurement results obtained by using the WSS but are somewhat lower.

研究了表征PCDD/Fs空气-水交换特性的传质系数。利用水面采样器（WSS）进行了蒸发和溶解氧（DO）实验，并在此基础上建立了半挥发性有机化合物（SVOCs）的MTCs模型。利用WSS观测到的蒸发通量进行计算，确定了平均空侧单个MTC [kg (H2O)]为1.33±0.58 cm/s kg (H2O)，其值与u10相关，并推导了WSS的模型。用通量和吸收两种方法计算了氧在水侧的单个MTC [(kw (O2)]。根据计算结果，通过通量法和吸收法测定的kw （O2）值分别为9.35 × 10−4±7.65 × 10−1 cm/s和1.30 × 10−3±6.60 × 10−1 cm/s。得到的kw（O2）值也与u10相关，并建立了WSS的kw(O2)_flux和kw(O2)_abs模型。因此，将研究结果应用于双膜理论，可计算出PCBs气相中总MTC [Kg (SVOCs)]为0.03±0.03 cm/s， PAHs为0.19±0.18 cm/s， OCPs为0.11±0.08 cm/s， PCDD/Fs为0.07±0.01 cm/s。MTC值呈现季节变化，在寒冷期较高。SVOCs种间的MTCs也存在振荡。随着氯数和分子量的增加，MTCs也随之增加。通过建模得到的结果与使用WSS得到的测量结果一致，但略低。

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引用次数: 0

Using the AP-42 model to estimate dust emission factors for high traffic roads 利用AP-42模型估算高流量道路扬尘排放因子

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-09-01 DOI: 10.1016/j.apr.2025.102727

Yifan Ding , Amir Saeidi , Tianyi Wang , Hongpufan Huang , Olivia Halseth , Francesca Hopkins , Akula Venkatram

Road dust emissions, predominantly comprising PM10, are significant contributors to ambient particulate matter (PM) concentrations in urban areas. The emission factor model recommended by the U.S. Environmental Protection Agency (USEPA) in AP-42 is based on data that exclude high-traffic roads such as California freeways. This study addresses this limitation by: (1) developing a mobile platform for measuring PM2.5 and PM10 emission factors on high-traffic roads, and (2) proposing a mechanistic model to overcome the shortcomings of the AP-42 model.

The mobile measurement platform combined PurpleAir particulate-matter sensors, meteorological instruments, and a custom dust sampler to collect real-time data without interrupting traffic. Field campaigns spanned eight California roadways, six freeways and two urban arterials. Observations of PM concentrations, silt loading, micrometeorology, and traffic flow were used both to test the U.S. EPA AP-42 road-dust model on heavily trafficked roads and to develop a physics-based alternative. With site-specific silt loading, the AP-42 equation produced credible PM2.5 and PM10 emission factors. The mechanistic model, which omits silt loading, yielded PM₁₀ emission factors comparable in accuracy to those from AP-42.

Implication

This study provides improved methods to measure and model PM10 and PM2.5 emissions from urban roads. Current AP-42 models are unreliable for high-traffic roads due to their dependence on silt loading values, which require traffic disruptions to measure accurately. The proposed mobile platform eliminates this challenge, and the mechanistic model offers an alternative to the empirical AP-42 approach. These innovations contribute significantly to advancing air quality management in urban areas with heavy vehicular traffic.

道路粉尘排放主要由可吸入颗粒物（PM10）组成，是城市地区环境颗粒物（PM）浓度的重要贡献者。美国环境保护署（USEPA）在AP-42中推荐的排放因子模型是基于不包括加州高速公路等高流量道路的数据。本研究通过以下方法解决了这一问题：(1)开发了一个移动平台来测量高流量道路上的PM2.5和PM10排放因子；(2)提出了一个机制模型来克服AP-42模型的不足。移动测量平台结合了PurpleAir颗粒物传感器、气象仪器和定制粉尘采样器，在不中断交通的情况下收集实时数据。现场活动横跨加州的8条公路，6条高速公路和2条城市主干道。通过对PM浓度、泥沙负荷、微气象学和交通流量的观测，在交通繁忙的道路上测试美国环保署AP-42道路粉尘模型，并开发基于物理的替代方案。在不同场地泥沙荷载下，AP-42方程得到可信的PM2.5和PM10排放因子。该机制模型忽略了泥沙荷载，其PM10排放因子的准确性与AP-42模型相当。本研究为城市道路PM10和PM2.5排放的测量和建模提供了改进的方法。目前的AP-42模型在高流量道路上是不可靠的，因为它们依赖于淤泥加载值，这需要交通中断才能准确测量。提出的移动平台消除了这一挑战，并且机制模型提供了经验AP-42方法的替代方案。这些创新为推进城市交通繁忙地区的空气质量管理做出了重大贡献。

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引用次数: 0

Quantifying the direction and distance characteristics of regional transport contribution to achieve joint emission controls of PM2.5 and O3 in Hefei 量化区域交通贡献的方向和距离特征，实现合肥市PM2.5和O3的联合排放控制

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-09-02 DOI: 10.1016/j.apr.2025.102731

Kangjia Gong, Lin Huang, Wenxing Fu, Yongliang She, Jianlin Hu

Regional transport has important impacts on fine particulate matter (PM_2.5) and ozone (O₃) pollution. Quantifying the direction and distance characteristics of regional transport contribution is crucial to formulating effective regional joint prevention and control policies. This study utilized the source-oriented WRF/CMAQ chemical transport model to quantify the contributions from various regions, categorized by direction and distance within Hefei and its surrounding areas, to PM_2.5 and O₃ in Hefei. The results indicate that local emissions in Hefei contributed 39 % during the modeled PM_2.5 pollution episode, while other areas in Anhui Province contributed 21 %. For transport distances of 50, 100 and 150 km, contributions were 12.9 %, 8.6 %, and 4.8 %, respectively, with eastern transport in these regions showing a higher impact, and contributions from areas 150 km away comprising 34.3 %. During O₃ pollution events, Hefei's local contribution to non-background O₃ was 62.5 %, while contributions from other areas in Anhui Province were primarily from western Anhui by 7.2 % and other regions by 6.4 %. By directional and distance analysis, northern Hefei contributed 30.5 %, western Anhui 25.2 %, with distances of 50, 100 and 150 km contributing 9.5 %, 5.6 %, and 4.3 %, and transport from 150 km away accounting for 16 %. For PM_2.5, sulfate and nitrate were mainly affected by long-range transport (>150 km), while primary particles and secondary organic aerosols were largely from local emissions (<50 km). For O₃, the formation contributions attributed by NOx were driven by regional transport, whereas the contributuions attributed by volatile organic compounds (VOC) were dominated by local emissions.

区域交通对细颗粒物（PM2.5）和臭氧（O3）污染有重要影响。量化区域交通贡献的方向和距离特征，对于制定有效的区域联防联控政策至关重要。本研究利用面向源的WRF/CMAQ化学输运模型，量化了合肥及周边地区各区域对PM2.5和O3的贡献，并按方向和距离进行了分类。结果表明，在模拟的PM2.5污染事件中，合肥的局部排放贡献了39%，而安徽省其他地区贡献了21%。对于50、100和150 km的运输距离，贡献分别为12.9%、8.6%和4.8%，其中东部运输对这些区域的影响更大，150 km的贡献占34.3%。在O3污染事件中，合肥对非本底O3的贡献为62.5%，而安徽省其他地区对非本底O3的贡献主要来自皖西地区，占7.2%，其他地区占6.4%。从方向和距离分析来看，合肥北部占30.5%，皖西占25.2%，其中50、100和150 km距离占9.5%、5.6%和4.3%，150 km距离占16%。对于PM2.5，硫酸盐和硝酸盐主要受远程输送（>150 km）的影响，而一次颗粒和二次有机气溶胶主要来自局地排放（<50 km）。对于O3， NOx对形成的贡献主要由区域运输驱动，而挥发性有机化合物（VOC）对形成的贡献主要由局部排放驱动。

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引用次数: 0

Impact of residential combustion on black carbon levels in Palapye, Botswana: Field measurements and GEOS-chem simulations 博茨瓦纳Palapye地区住宅燃烧对黑碳水平的影响：现场测量和地球物理系统化学模拟

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-20 DOI: 10.1016/j.apr.2025.102707

Janica N.D. Gordon , Kelsey R. Bilsback , Marc N. Fiddler , Jeffrey R. Pierce , Gizaw Mengistu Tsidu , Solomon Bililign

Over half of the population in Africa still rely on solid fuels such as wood, coal, dung, crop waste, and charcoal for household heating and cooking. Combustion of such fuels leads to high levels of PM_2.5 emissions, where a large fraction of PM_2.5 is composed of black carbon (BC) and organic carbon (OA). Additionally, there is a lack of continuous ground-based monitors in Africa to measure emissions and chemical composition, which is essential for monitoring changes in air quality. To better understand air pollution in Africa, we conducted a five-week field campaign in June and July of 2022 at Botswana International University of Science and Technology (BIUST) located in Palapye, Botswana and ran numerous GEOS-Chem simulations to understand which anthropogenic sources had the potential to impact the field-measured BC. BC field measurements were collected using a micro-aethalometer. Simulations were used to quantify the effect of four anthropogenic sources (energy, industry, residential, and transportation) on ambient BC in Southern Africa during June and July of 2018 and 2022. The average BC concentrations from field measurements at BIUST were 0.34 μg m⁻³. GEOS-Chem, simulation results showed that those residential emissions contributed 52 % and 49 % of the average ambient BC during both months in 2018 and 2022, respectively, at the observation site at BIUST. Compared to the other three combustion sources, residential emissions contributed the largest to the average ambient BC concentrations in this region.

非洲一半以上的人口仍然依赖固体燃料，如木材、煤炭、粪便、农作物废料和木炭来进行家庭取暖和烹饪。这些燃料的燃烧导致PM2.5的高水平排放，其中PM2.5的很大一部分由黑碳（BC）和有机碳（OA）组成。此外，非洲缺乏连续的地面监测仪来测量排放和化学成分，这对监测空气质量的变化至关重要。为了更好地了解非洲的空气污染，我们于2022年6月和7月在位于博茨瓦纳Palapye的博茨瓦纳国际科技大学（BIUST）进行了为期五周的实地调查，并进行了大量的GEOS-Chem模拟，以了解哪些人为来源有可能影响实地测量的BC。BC现场测量使用微血压计收集。模拟用于量化2018年6月和2022年7月期间四个人为来源（能源、工业、住宅和交通）对南部非洲环境BC的影响。BIUST现场测量的BC平均浓度为0.34 μg m−3。GEOS-Chem的模拟结果显示，2018年和2022年两个月，北京科技大学观测点的居民排放分别占平均环境碳排放量的52%和49%。与其他三种燃烧源相比，居民排放对该地区平均环境BC浓度的贡献最大。

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引用次数: 0

Drivers of fine particulate matter improvement and ozone increase in Shandong, China from 2013 to 2017 2013 - 2017年中国山东省细颗粒物改善和臭氧增加的驱动因素

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-29 DOI: 10.1016/j.apr.2025.102724

Na Zhao , Sen Yao

The air quality in China improved during 2013–2017 due to the implementation of the strictest clean air actions in the country's history. However, the driving factors behind the improvement in air quality in Shandong Province remain unclear. To address this challenge, we designed eight sensitivity experiments and conducted 32 simulations using the Weather Research and Forecasting-Comprehensive Air Quality Model with Extensions (WRF-CAMx) to quantify the contributions of emission reductions and meteorological conditions to changes in fine particulate matter (PM_2.5) and ozone (O₃) concentrations. Emissions of most major pollutants decreased substantially, while volatile organic compounds increased by 4.5 % during 2013–2017. Emission reductions and meteorological variations contributed to a 19.6 % and 9.5 % decrease in PM_2.5 concentrations, respectively, with emission control measures proving most effective in winter and meteorological conditions showing contrasting seasonal influences. Sectoral analysis revealed varying contributions to PM_2.5 improvements, with residential sources having the most significant impact, particularly in winter, driven by the expansion of centralized heating coverage and clean energy initiatives. The increase in O₃ concentrations was driven by both emission reductions and meteorological variations, resulting in increases of 17.4 % and 7.3 %, respectively. Emission reductions drove O₃ increases across all seasons, while meteorological conditions exhibited varying seasonal impacts on O₃ concentrations. Spring conditions contributed to an 8.9 % improvement in O₃ concentrations, while other seasons experienced rebounds. Nearly all emission reductions contributed to O₃ increases, with residential and industrial sources showing the most substantial impacts. These founding can provide support for the refined control of PM_2.5 and O₃ pollution.

由于实施了中国历史上最严格的清洁空气行动，2013-2017年中国的空气质量有所改善。然而，山东省空气质量改善背后的驱动因素尚不清楚。为了应对这一挑战，我们设计了8个敏感性实验，并使用天气研究与预报-综合空气质量模型扩展（WRF-CAMx）进行了32次模拟，以量化减排和气象条件对细颗粒物（PM2.5）和臭氧（O3）浓度变化的贡献。2013-2017年，大多数主要污染物排放量大幅下降，挥发性有机物排放量增长4.5%。减排和气象变化对PM2.5浓度的贡献分别为19.6%和9.5%，其中排放控制措施在冬季最为有效，而气象条件的季节性影响则截然不同。行业分析显示，PM2.5的改善有不同的贡献，在集中供暖覆盖范围扩大和清洁能源倡议的推动下，住宅污染源的影响最为显著，尤其是在冬季。O3浓度的增加是由减排和气象变化共同驱动的，分别增加了17.4%和7.3%。排放减少驱动O3在所有季节的增加，而气象条件对O3浓度的影响表现出不同的季节性。春季条件对臭氧浓度的改善贡献了8.9%，而其他季节则出现反弹。几乎所有的减排都导致了臭氧的增加，其中住宅和工业排放的影响最大。这些发现可以为PM2.5和O3污染的精细化控制提供支持。

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引用次数: 0

Advanced NOx emission modeling for diesel trucks: Incorporating exhaust temperature and load dynamics 先进的氮氧化物排放建模柴油卡车：纳入排气温度和负载动态

IF 3.5 3区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Atmospheric Pollution Research

Pub Date : 2026-01-01 Epub Date: 2025-08-18 DOI: 10.1016/j.apr.2025.102703

Xin Wang , Jianchang Huang , Xue Lei , Xianfei Yue , Leqi Zhang , Shubin Li , Huanqin Yu , Shenzhen Ding

Heavy-duty diesel trucks (HDDTs), although a small portion of the total vehicle population in China, are a significant source of air pollution. The selective catalytic reduction (SCR) systems employed to reduce NOx in these trucks depend largely on exhaust temperature, which varies with vehicle load and operating conditions. However, most existing emission models do not consider these dynamic factors, resulting in inaccurate NOx emission predictions under different load conditions. This study proposes a novel NOx emission modeling framework that integrates exhaust temperature behavior and real-time load dynamics into the estimation process. The model couples a NOx conversion rate module with a physically grounded exhaust temperature model, both parameterized by vehicle operating power. By integrating a NOx conversion rate model with an exhaust temperature model, this research offered a more accurate framework for estimating NOx emissions across various load conditions and speeds. Quantitatively, the model reduced NOx emission errors compared to traditional methods by 5.4 % for no-load, 16.6 % for half-load, and 14.8 % for full-load conditions. The study also investigated NOx emission characteristics under different load conditions, identifying key intersections and inverse correlations in emission factors at different speeds. These findings highlight the model's enhanced predictive ability under complex, real-world driving conditions. Overall, this study enhanced the accuracy of emission estimates and supported the development of more effective environmental regulatory strategies.

重型柴油卡车（HDDTs）虽然只占中国车辆总数的一小部分，但却是空气污染的重要来源。这些卡车采用选择性催化还原（SCR）系统来减少氮氧化物，这在很大程度上取决于排气温度，而排气温度随车辆负载和操作条件的变化而变化。然而，现有的排放模型大多没有考虑这些动态因素，导致不同负荷条件下的NOx排放预测不准确。本研究提出了一种新的氮氧化物排放建模框架，该框架将排气温度行为和实时负载动态集成到估计过程中。该模型将氮氧化物转化率模块与物理接地排气温度模型耦合在一起，两者都由车辆运行功率参数化。通过将NOx转化率模型与排气温度模型相结合，本研究为估算不同负载条件和速度下的NOx排放量提供了更准确的框架。从数量上看，与传统方法相比，该模型在空载、半载和满载情况下分别将NOx排放误差降低了5.4%、16.6%和14.8%。研究还考察了不同负荷工况下的NOx排放特征，确定了不同车速下的关键交叉口及排放因子的负相关关系。这些发现凸显了该模型在复杂的真实驾驶条件下的预测能力。总的来说，这项研究提高了排放估计的准确性，并支持制定更有效的环境监管战略。

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引用次数: 0